1. U.S. aerosols: observation from space, interactions with climate
Daniel J. Jacob
with Easan E. Drury, Loretta J. Mickley,
Eric M. Leibensperger, Amos Tai
and funding from NASA, EPRI, EPA

2. SATELLITE OBSERVATIONS OF TROPOSPHERIC COMPOSITION:
a revolution over the past decade
The NASA “A-Train”
Integrated observing system
Satellites
Models
aircraft, ships,
sondes, lidars
Surface sites
Principal tropospheric species measured from space:
Ozone , NO2, formaldehyde, BrO, glyoxal
CO, CO2, methane
Aerosols, SO2

3. AEROSOL CHARACTERISTICS
number
AEROSOL CHARACTERISTICS
area
Typical size distribution
(Seinfeld and Pandis, 1998)
volume
PM2.5 (EPA std.)
Chemical composition of PM2.5
(NARSTO, 2004)
sulfate (coal combustion)
nitrate (fossil fuel combustion)
ammonium (agriculture)
black carbon (combustion)
organic carbon (combustion,
vegetation)
soil
other

4. HOW TO OBSERVE AEROSOLS FROM SPACE?
Solar occultation
(SAGE, POAM…)
Active system
(CALIPSO…)
Solar back-scatter
(MODIS, MISR…)
laser
pulse
EARTH
Surface Surface
Pros: high S/N, vertical profiling
Cons: sparse sampling,
cloud interference,
low horizontal resolution
Pro: vertical profiling
Con: sparse sampling
Pro: horiz. resolution
Con: daytime and column only

5. Aerosol observation from space by solar backscatter
Easy to do qualitatively for thick plumes over ocean…
California fire plumes
Pollution off U.S. east coast
Dust off West Africa
…but difficult quantitatively! Fundamental quantity is aerosol optical depth (AOD)
Il ()
Measured top-of-atmosphere reflectance
= f (AOD, aerosol properties,
surface reflectance, air scattering,
gas absorption, Sun-satellite geometry)
aerosol
scattering,
absorption
Il (0)=Il ()exp[-AOD]

6. Aerosol optical depths (AODs) measured from space
Jan 2001 – Oct 2002 operational data
MODIS (c004)
return time 2x/day;
nadir view
known positive bias
over land
550 nm
AODs
MISR
9-day return time;
multi-angle view
better but much sparser
van Donkelaar et al. [2006]

7. MODIS OPERATIONAL RETRIEVAL OVER LAND
Use top-of-atmosphere (TOA) reflectance
at 2.13 mm (transparent atmosphere) to derive surface reflectance
Assume fixed 0.47/2.13 and 0.65/2.13 surface reflectance ratios to derive atmospheric reflectances at 0.47 and 0.65 mm by subtraction
Assume generic aerosol optical properties to convert atmospheric reflectance to AOD
MISR does along-track multi-angle viewing of same aerosol column – better constraints but sparser data
TOA
reflectance
0.47 mm
0.65 mm
2.13 mm
SURFACE
Y. Kaufman, L. Remer, and MODIS Science Team

8. IMPROVING THE MODIS AEROSOL RETRIEVAL USING ICARTT AIRCRAFT DATA OVER US (Jul-Aug 2004)
fit AODs
synthetic TOA
reflectance = f(AOD,…)
MODIS satellite instrument:
TOA reflectance
NASA, NOAA, DOE aircraft:
speciated mass concentrations,
microphysical & optical properties
GEOS-Chem
model
evaluate
NASA
DC-8
MODIS local
surface
reflectance
and ratio
EPA AQS/IMPROVE surface networks: mass concentrations
NASA AERONET surface network: AODs
EASTERN U.S.
Drury et al. [JGR 2008, in prep]

9. IMPROVING THE SURFACE REFLECTANCE CORRECTION FOR MODIS AEROSOL RETRIEVALS
2.13 mm
0.65 mm
Measured
top-of-atmosphere (TOA)
reflectances
(ICARTT period)
0.65/2.13 surface reflectance ratio
Measured 0.65 vs. 2.13
TOA reflectances: take lower envelope for given location to derive surface reflectance ratio
Fresno, CA
ICARTT period
Derive aerosol reflectance
at 0.65 mm
(same procedure for 0.47 mm)
Drury et al. [JGR 2008]

10. CONVERTING TOA AEROSOL REFLECTANCES TO AODs
Standard MODIS algorithm assumes generic aerosol optical properties
Better way is to use local info for given scene from a global 3-D aerosol model
Use GEOS-Chem model driven by NASA/GEOS assimilated meteorological data with 2ox2.5o resolution
Model simulates mass concentrations of different aerosol types
Size distributions and optical properties for different aerosol types are assumed (test with ICARTT data)
Calculate TOA reflectances from model fields to compare with MODIS
Consistency of aerosol optical properties enables subsequent model evaluation with observed MODIS AODs

11. PREVIOUS MODEL EVALUATION: sulfate-nitrate-ammonium
Annual mean concentrations at IMPROVE sites (2001) – CASTNET for NH4+
r = 0.96 bias = +10% r = bias = +30% r =0.94 bias = +10%
Sulfate is 100% in aerosol;
Ammonia NH3(g) neutralizes sulfate to form (NH4)2SO4;
Excess NH3(g) if present can combine with HNO3(g) to form NH4NO3
as function of T, RH
Park et al. [AE 2006]

12. PREVIOUS MODEL EVALUATION: carbonaceous aerosol
Annual mean concentrations at IMPROVE sites (2001)
Elemental carbon (EC) Organic carbon (OC)
r = bias = -15% r = bias = +20%
Primary sources: fossil fuel, biofuel, wildfires
Also large growing-season biogenic source of secondary organic aerosol (SOA)
volatile organic
compounds (VOCs)
oxidation, multi-step
SOA
Park et al. [AE 2006]

13. PREVIOUS MODEL EVALUATION: mineral dust
Annual mean concentrations at IMPROVE sites (2001)
GEOS-Chem
Asian dust
Saharan
dust
Local
Fairlie et al. [AE 2007]

14. AEROSOL VERTICAL PROFILES IN ICARTT
bulk filter (Dibb, UNH)
IMPROVE (<2.5 mm)
NASA DC-8
PILS (Weber, GIT)
Sulfate model overestimate: excessive cloud processing?
Unresolved issue with aircraft dust observations at low altitude
Easan Dury, in prep.

15. ORGANIC AEROSOL IN ICARTT
PILS water-soluble organic carbon (WSOC) on NOAA P-3
IMPROVE measurements of organic carbon
Fu et al.
(AE, 2009)
Standard reversible SOA (Pankow/Seinfeld):
Dicarbonyl SOA (Liggio/Fu):

16. MEAN AEROSOL VERTICAL PROFILES IN ICARTT
Bulk of mass is in boundary layer below 3 km: mostly sulfate, organic
Dust, organic dominate above 3 km
Easan Drury, in prep.

17. AEROSOL OPTICAL PROPERTIES IN ICARTT
Single-scattering albedo = fraction of
aerosol extinction due to scattering
standard model
assumption (GADs)
improved fit
(this work)
AERONET
Easan Drury, in prep.

18. MEAN AEROSOL OPTICAL DEPTHS DURING ICARTT
Model results compared to observations from AERONET network (circles)
Model w/ GADs size distributions Model w/improved size distributions
r = bias = -21% r = bias = -7%
Main improvement was to reduce the geometric standard deviation in the log-normal size distributions for sulfate and OC from 2.2 to 1.6
Easan Drury, in prep.

19. AEOSOL OPTICAL DEPTHS (0.47 mm), JUL-AUG 2004
c004 and c005 are the MODIS operational data; AERONET data are in circles
GEOS-Chem model MODIS (this work)
MODIS (c004) MODIS (c005)
Beyond improving on the operational products, our MODIS retrieval enables quantitative comparison to model results (consistent aerosol optical properties)
Results indicate model underestimate in Southeast US – organic aerosol
Drury et al., in prep.

20. Can we use AODs measured from space as proxy for PM2.5?
Infer PM2.5 from AOD by
MODIS PM2.5 (this work)
EPA AQS surface network data
Drury et al.
[in prep]
Sulfate observations in Pittsburgh
(Wittig et al. 2004)
Bias in source
regions could be partly due to PM2.5 diurnal cycle:
MODIS

21. RADIATIVE FORCING OF CLIMATE BY AEROSOLS
Globally averaged radiative forcing due to CO2 is +1.7 Wm-2
Liao et al. , 2004
Over eastern US, radiative forcing due to sulfate aerosols is -2 Wm-2
IPCC (2007)
US sulfur emissions are decreasing rapidly:
what are the impacts on the regional climate?
today

22. CALCULATING THE CLIMATE RESPONSE FROM SHUTTING DOWN U.S. AEROSOL
GISS GCM
Consider two scenarios:
Control: aerosol optical depths fixed at 1990s levels.
Sensitivity: U.S. aerosol optical depths set to zero (radiative forcing of about +2 W m-2 over US)
Conduct ensemble of 3 simulations for each scenario.
Mickley et al. (in prep.)

23. Removal of anthropogenic aerosols over US leads to a 0
Removal of anthropogenic aerosols over US leads to a 0.5-1o C warming in annual mean surface temperature.
Warming due to trend in greenhouse gases.
Additional warming due to zeroing of aerosols over the US.
The trend in greenhouse gases leads to warming surface temperatures in both the Control (left panel) and the No-US-aerosol model simulation. As expected, temperatures for both cases rise most rapidly at high northern latitudes, as much as 1.5oC. For the No-US-aerosol case, we find additional warming over the eastern US, where the aerosol optical depths in the Control are greatest (right panel). This additional warming (+0.5 C, annual mean) is about the same in magnitude as the warming in this region due to the trend in greenhouse gases. In effect, the removal of the US anthropogenic aerosols doubles the warming experienced over the eastern US in this time frame.
Annual mean surface temperature change in Control.
Mean temperature difference:
No-US-aerosol case – Control
White areas signify no significant difference.
Results from an ensemble of 3 for each case.
Mickley et al., in prep.

24. Regional surface temperature response to aerosol removal is a persistent effect
Temperature (oC)
No-US-aerosols case
Control, with US aerosols
Annual mean temperature trends over Eastern US
The calculated surface temperature response in the No-US-aerosol case persists over time. The plots shows the surface temperature trend for 2010 to 2040 averaged over the Northeast US for the two simulations: the Control (green) and the No-US-aerosol case (blue). The solid curves show the trends in annual mean temperatures, the dashed curves show the 9-year running means.
Mickley et al., in prep.

25. EFFECT OF FUTURE CLIMATE CHANGE ON US AIR QUALITY
Models show consistent increase of ozone, mainly driven by temperature
Results from six coupled GCM-CTM simulations
Northeast Midwest California Texas Southeast
change of 8-h daily max ozone in summer,
keeping anthropogenic emissions constant
ppb
Weaver et al.
[BAMS, submitted]
…but model results for aerosols show no such consistency, including in sign.
How can we progress?

26. AEROSOL CORRELATION WITH METEOROLOGICAL VARIABLES
Multilinear regression model fit to
deseasonalized EPA/AQS data
for PM2.5 (total and speciated)
mostly precipitation
mostly temperature and stagnation R2
R2 fit
Tai et al.
[in prep.]

27. TEMPERATURE COEFFICIENTS FOR SPECIATED PM2.5
Positive association of nitrate with temperature in California could be driven by ammonia emissions
Tai et al. [in prep.]

28. WIND VECTOR COEFFICIENTS FOR SPECIATED PM2.5
Tai et al. [in prep.]